Instructions to use Tanor/BERTicSENTNEG6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tanor/BERTicSENTNEG6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tanor/BERTicSENTNEG6")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tanor/BERTicSENTNEG6") model = AutoModelForSequenceClassification.from_pretrained("Tanor/BERTicSENTNEG6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 21f729c89c1edc12ee3dbe7f0322076297941a2ad1285a7521bb64b81f73e2bd
- Size of remote file:
- 4.98 kB
- SHA256:
- 515c367d262e7dbd09c3a8418774e26e78c9d9326da0a2f5ed8705a7cc1a57fd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.